Prescriptions

Mantis-ml: Disease-Agnostic Gene Prioritization from High-Throughput Genomic Screens by Stochastic Semi-supervised Learning.

Access to large-scale genomics datasets has increased the utility of hypothesis-free genome-wide analyses. However, gene signals are often insufficiently powered to reach experiment-wide significance, triggering a process of laborious triaging of genomic-association-study results. We introduce mantis-ml, a multi-dimensional, multi-step machine-learning framework that allows objective assessment of...

Artificial Intelligence in Diagnostic Imaging: Status Quo, Challenges, and Future Opportunities.

In this review article, the current and future impact of artificial intelligence (AI) technologies on diagnostic imaging is discussed, with a focus on cardio-thoracic applications. The processing of imaging data is described at 4 levels of increasing complexity and wider implications. At the examination level, AI aims at improving, simplifying, and standardizing image acquisition and processing. S...

Flexible and stretchable dual mode nanogenerator for rehabilitation monitoring and information interaction.

Motion recognition and information interaction sensors with flexibility and stretchability are key functional modules as interactive media between the...

medExtractR: A targeted, customizable approach to medication extraction from electronic health records.

OBJECTIVE: We developed medExtractR, a natural language processing system to extract medication information from clinical notes. Using a targeted appr...

Embryo through the lens: from time-lapse cinematography to artificial intelligence.

"Once a new technology rolls over you, if you're not part of the steamroller, you're part of the road." -Stewart Brand.

Ensembled machine learning framework for drug sensitivity prediction.

Drug sensitivity prediction is one of the critical tasks involved in drug designing and discovery. Recently several online databases and consortiums h...

Evaluation of clinical impact of pharmacogenomics knowledge involved in CPIC guidelines on Chinese pediatric patients.

To evaluate the clinical benefits of implementing pharmacogenomics testing for Chinese pediatric patients.   Based on the drug-gene interactions invo...

Development of an Artificial Intelligence Model to Identify a Dental Implant from a Radiograph.

PURPOSE: The objective of this study was to develop a deep convolutional neural network (CNN) that would identify the brand and model of a dental impl...

Single Center Experience with Robot Technologies for Sterile Compounding: A Retrospective Review.

The compounding of sterile medication admixtures is a labor-intensive process and subject to potential human error. The addition of robotic devices an...

Trajectory tracking control of 7-DOF redundant robot based on estimation of intention in physical human-robot interaction.

Collaborative robot has been widespread application prospect, such as homes, manufacturing, and health-care etc. In physical human-robot interaction, ...

Machine Intelligence in Cardiovascular Medicine.

The computer science technology trend called artificial intelligence (AI) is not new. Both machine learning and deep learning AI applications have rec...

A study of deep learning approaches for medication and adverse drug event extraction from clinical text.

OBJECTIVE: This article presents our approaches to extraction of medications and associated adverse drug events (ADEs) from clinical documents, which ...

An ensemble of neural models for nested adverse drug events and medication extraction with subwords.

OBJECTIVE: This article describes an ensembling system to automatically extract adverse drug events and drug related entities from clinical narratives...

Ensemble method-based extraction of medication and related information from clinical texts.

OBJECTIVE: Accurate and complete information about medications and related information is crucial for effective clinical decision support and precise ...

Adverse drug event and medication extraction in electronic health records via a cascading architecture with different sequence labeling models and word embeddings.

OBJECTIVE: An adverse drug event (ADE) refers to an injury resulting from medical intervention related to a drug including harm caused by drugs or fro...

Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods.

OBJECTIVE: Identification of drugs, associated medication entities, and interactions among them are crucial to prevent unwanted effects of drug therap...

2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records.

OBJECTIVE: This article summarizes the preparation, organization, evaluation, and results of Track 2 of the 2018 National NLP Clinical Challenges shar...

Extracting medications and associated adverse drug events using a natural language processing system combining knowledge base and deep learning.

OBJECTIVE: Detecting adverse drug events (ADEs) and medications related information in clinical notes is important for both hospital medical care and ...

Application of Augmented Intelligence for Pharmacovigilance Case Seriousness Determination.

INTRODUCTION: Identification of adverse events and determination of their seriousness ensures timely detection of potential patient safety concerns. A...

Targeted prescription of cognitive-behavioral therapy versus person-centered counseling for depression using a machine learning approach.

OBJECTIVE: Depression is a highly common mental disorder and a major cause of disability worldwide. Several psychological interventions are available,...